update readme

README.md

@@ -57,11 +57,7 @@ cd ../../
 ```
 
 # Model zoo
-
-EasyAnimateV3:
-| Name | Type | Storage Space | Url | Hugging Face | Description |
-|--|--|--|--|--|--|
-| EasyAnimateV3-XL-2-InP-512x512.tar | EasyAnimateV3 | 16.2GB | [Download](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/EasyAnimateV4:
+EasyAnimateV4:
 
 We attempted to implement EasyAnimate using 3D full attention, but this structure performed moderately on slice VAE and incurred considerable training costs. As a result, the performance of version V4 did not significantly surpass that of version V3. Due to limited resources, we are migrating EasyAnimate to a retrained 16-channel MagVit to pursue better model performance.
 
@@ -69,6 +65,7 @@ We attempted to implement EasyAnimate using 3D full attention, but this structur
 |--|--|--|--|--|--|
 | EasyAnimateV4-XL-2-InP.tar.gz | EasyAnimateV4 | Before extraction: 8.9 GB \/ After extraction: 14.0 GB | [Download](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Diffusion_Transformer/EasyAnimateV4-XL-2-InP.tar.gz) | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV4-XL-2-InP)| Our official graph-generated video model is capable of predicting videos at multiple resolutions (512, 768, 1024, 1280) and has been trained on 144 frames at a rate of 24 frames per second. |
 
+
 # Algorithm Detailed
 ### 1. Data Preprocessing
 **Video Cut**